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Journals / Avrupa Bilim ve Teknoloji Dergisi / 2021 / Sayı: 29

A Palm Vein Recognition Approach by Multiple Convolutional Neural Network Models

Çoklu Evrişimli Sinir Ağı Modelleri İle Bir Avuç İçi Damar Tanıma Yaklaşımı

Felix Olanrewaju BABALOLA
Önsen TOYGAR
Yiltan BİTİRİM
Pages
237–242
Publication Date
2021-12-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed system significantly increases the accuracy of the system in comparison with the individual models and achieves 99.06 %, 99.83 % and 99.26 % on FYO, PUT and VERA datasets, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:A palm vein recognition system is proposed in this paper.
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Key Findings & Data:The efficiency of three convolutional neural network models (VGG16, VGG19 and AlexNet) in palm vein biometrics is compared and then this study proposes to fuse them with Decision-Level Fusion.
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Conclusion & Impact:The proposed system significantly increases the accuracy of the system in comparison with the individual models and achieves 99.06 %, 99.83 % and 99.26 % on FYO, PUT and VERA datasets, respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Biyometri · Avuç içi damarı · VGG16

Abstract

A palm vein recognition system is proposed in this paper. The efficiency of three convolutional neural network models (VGG16, VGG19 and AlexNet) in palm vein biometrics is compared and then this study proposes to fuse them with Decision-Level Fusion. These models employ the use of high number of filters during training which leads to very high computation time, therefore, the filters are reduced in this study to drastically reduce computation time while maintaining the efficiency of the models. The proposed method is tested on three datasets secured from FYO, PUT and VERA databases. The proposed system significantly increases the accuracy of the system in comparison with the individual models and achieves 99.06 %, 99.83 % and 99.26 % on FYO, PUT and VERA datasets, respectively.

Özet

Bu makalede bir avuç içi damar tanıma sistemi önerilmiştir. Üç Evrişimli Sinir Ağı (CNN) modelinin (VGG16, VGG19 ve AlexNet) avuç içi damar biyometrisindeki etkisi karşılaştırılmış ve daha sonra bu modellerin Karar-Seviyesi Kaynaşımı kullanarak birleştirilmesi önerilmiştir. Bu modellerin eğitiminde çok fazla süzgeç kullanıldığı için hesaplama süresi çok yüksektir. Dolayısıyla, bu çalışmada, modellerin verimliliğini muhafaza ederken hesaplama süresini de büyük ölçüde azaltmak için modellerde kullanılan süzgeçler azaltılmıştır. Önerilen yöntem, FYO, PUT ve VERA isimli üç halka açık veritabanı kullanılarak test edilmiştir. Bireysel modellerle kıyaslandığında, önerilen yöntemin doğruluğu önemli ölçüde artmıştır ve FYO, PUT ve VERA veri kümeleri üzerinde sırasıyla %99.06, %99.8 ve %99.26 başarı elde edilmiştir.

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Cite This Article

BABALOLA, F. O., TOYGAR, Ö., BİTİRİM, Y. (2021). A Palm Vein Recognition Approach by Multiple Convolutional Neural Network Models. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 29, 237–242. https://ajindex.com/articles/470633

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References (9)

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